OBJECTIVE: To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh.
METHODS: Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values.
RESULTS: Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model's predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly.
CONCLUSIONS: This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.
METHODS: We searched PubMed, Web of Science, and Scopus as of 1st June 2023. We performed a systematic review and meta-analysis of pooled POTS rate in SARS-CoV-2-infected and COVID-19-vaccinated groups from epidemiological studies, followed by subgroup analyses by characteristic. Meta-analysis of risk ratio was conducted to compare POTS rate in infected versus uninfected groups. Meta-analysis of demographics was also performed to compare cases of post-infection and post-vaccination POTS from case reports and series.
RESULTS: We estimated the pooled POTS rate of 107.75 (95 % CI: 9.73 to 273.52) and 3.94 (95 % CI: 0 to 16.39) cases per 10,000 (i.e., 1.08 % and 0.039 %) in infected and vaccinated individuals based on 5 and 2 studies, respectively. Meta-regression revealed age as a significant variable influencing 86.2 % variance of the pooled POTS rate in infected population (P
PURPOSE: The purpose of this systematic review was to compare the cost-effectiveness and PROMs between digitally and conventionally fabricated complete dentures.
MATERIAL AND METHODS: An electronic search of publications from 2011 to mid-2023 was established using PubMed/Medline, EBSCOhost, and Google Scholar. Retrospective, prospective, randomized controlled, and randomized crossover clinical studies on at least 10 participants were included. A total of 540 articles were identified and assessed at the title, abstract, and full article level, resulting in the inclusion of 14 articles. Data on cost, number of visits, patient satisfaction, and oral health-related quality of life were examined and reported.
RESULTS: The systematic review included 572 digitally fabricated complete dentures and 939 conventionally fabricated complete dentures inserted in 1300 patients. Digitally fabricated complete dentures require less clinical time with a lower total cost, despite higher material costs compared with the conventional fabrication technique. Digitally and conventionally fabricated complete dentures were found to have significant effects on mastication efficiency, comfort, retention, stability, ease of cleaning, phonetics, and overall patient satisfaction, as well as social disability, functional limitation, psychological discomfort, physical pain, and handicap.
CONCLUSIONS: Digitally fabricated complete dentures are more cost-effective than conventionally fabricated dentures. There are various impacts of conventionally and digitally fabricated complete dentures on PROMs, and they are not better than one another.
METHODS: We searched five global databases (MEDLINE, Embase, CINAHL Plus, Global Health, WHO COVID-19) on 12 May 2022 and 28 July 2023 and three Chinese databases (CNKI, Wanfang, CQvip) on 16 October 2022 for articles reporting incidence and outcomes of SARS-CoV-2 reinfection before the period of Omicron (B.1.1.529) predominance. We assessed risk of bias using Joanna Briggs Institute critical appraisal tools and conducted meta-analyses with random effects models to estimate the proportion of SARS-CoV-2 reinfection among initially infected cases and hospitalisation and mortality proportions among reinfected ones.
RESULTS: We identified 7593 studies and extracted data from 64 included ones representing 21 countries. The proportion of SARS-CoV-2 reinfection was 1.16% (95% confidence interval (CI) = 1.01-1.33) based on 11 639 247 initially infected cases, with ≥45 days between the two infections. Healthcare providers (2.28%; 95% CI = 1.37-3.40) had a significantly higher risk of reinfection than the general population (1.00%; 95% CI = 0.81-1.20), while young adults aged 18 to 35 years (1.01%; 95% CI = 0.8-1.25) had a higher reinfection burden than other age groups (children <18 years old: 0.57%; 95% CI = 0.39-0.79, older adults aged 36-65 years old: 0.53%; 95% CI = 0.41-0.65, elderly >65 years old: 0.37%; 95% CI = 0.15-0.66). Among the reinfected cases, 8.12% (95% CI = 5.30-11.39) were hospitalised, 1.31% (95% CI = 0.29-2.83) were admitted to the intensive care unit, and 0.71% (95% CI = 0.02-2.01) died.
CONCLUSIONS: Our data suggest a relatively low risk of SARS-CoV-2 reinfection in the pre-Omicron era, but the risk of hospitalisation was relatively high among the reinfected cases. Considering the possibility of underdiagnosis, the reinfection burden may be underestimated.
REGISTRATION: PROSPERO: CRD42023449712.